Serratia (oligotyping)
Load packages, paths, functions
# Load main packages, paths and custom functions
source("../../../source/main_packages.R")
source("../../../source/functions.R")
# Load supplementary packages
packages <- c("RColorBrewer", "ggpubr", "cowplot", "Biostrings", "openxlsx", "kableExtra")
invisible(lapply(packages, require, character.only = TRUE))Preparation
Tables preparation
Seqtab
# move to oligotyping directory
path_serratia <- "../../../../output/2_Oligotyping/2A/Serratia/2A_oligotyping_Serratia_sequences-c1-s1-a0.0-A0-M10"
# load the matrix count table
matrix_count <- read.table(paste0(path_serratia, "/MATRIX-COUNT.txt"), header = TRUE) %>% t()
# arrange it
colnames(matrix_count) <- matrix_count[1,]
matrix_count <- matrix_count[-1,]
matrix_count <- matrix_count %>% as.data.frame()
# print it
matrix_count %>%
kbl() %>%
kable_paper("hover", full_width = F)| CTC1 | CTC10 | CTC11 | CTC12 | CTC13 | CTC14 | CTC15 | CTC2 | CTC3 | CTC4 | CTC5 | CTC6 | CTC7 | CTC9 | NP41 | S104 | S25 | S27 | S30 | S32 | S36 | S37 | S38 | S39 | S44 | S48 | S49 | S51 | S77 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A | 10 | 24 | 10 | 7 | 442 | 3 | 10 | 3 | 4 | 8 | 0 | 26 | 2 | 2 | 0 | 4 | 3 | 2 | 1200 | 965 | 55 | 2132 | 46 | 666 | 17 | 2 | 43 | 13 | 314 |
| G | 4 | 6 | 1 | 0 | 207 | 0 | 7 | 2 | 2 | 5 | 1 | 5 | 2 | 1 | 40 | 0 | 5 | 0 | 655 | 298 | 36 | 874 | 7 | 306 | 6 | 0 | 23 | 5 | 0 |
Taxonomy
# load the fasta table
fasta <- readDNAStringSet(paste0(path_serratia, "/OLIGO-REPRESENTATIVES.fasta"))
# arrange it
fasta <- fasta %>% as.data.frame()
colnames(fasta) <- "seq"
fasta$oligotype <- rownames(fasta)
fasta <- fasta %>% dplyr::select(-c(seq))
# print it
fasta %>%
kbl() %>%
kable_paper("hover", full_width = F)| oligotype | |
|---|---|
| A | A |
| G | G |
Change oligotype name by oligotype / MED nodes in the matrix count
# Reference file
## load the reference table
ref_oligo_med2 <- read.table("../../../../output/2_Oligotyping/2B/2B_REF_info_serratia.tsv", sep="\t", header = TRUE)
## select only the 2 oligotypes of Serratia
ref_oligo_med2 <- ref_oligo_med2[!is.na(ref_oligo_med2$oligotype),]
## change order of columns
ref_oligo_med2 <- ref_oligo_med2 %>% select(c(seq, oligotype, MED_node_frequency_size, OLIGO_oligotype_frequency_size))
## create a column with reference name (will be used in plots)
ref_oligo_med2$ref <- paste0("oligotype_", ref_oligo_med2$OLIGO_oligotype_frequency_size, " / node_", ref_oligo_med2$MED_node_frequency_size)
## create a copy of fasta
fasta2 <- fasta
# Matrix count
## create an oligotype column in the matrix count
matrix_count$oligotype <- rownames(matrix_count)
## change order of columns
matrix_count <- matrix_count %>% dplyr::select(c(oligotype, everything()))
## merge the matrix count and the reference dataframe
matrix_count2 <- matrix_count %>% merge(ref_oligo_med2 %>% dplyr::select(-c(seq)), by="oligotype")
## change order of columns
matrix_count2 <- matrix_count2 %>% dplyr::select(c(oligotype, MED_node_frequency_size, OLIGO_oligotype_frequency_size, ref, everything()))
## change rownames
rownames(matrix_count2) <- matrix_count2$ref
## change order of columns
matrix_count2 <- matrix_count2 %>% dplyr::select(-c(oligotype, ref, MED_node_frequency_size, OLIGO_oligotype_frequency_size))
## print it
matrix_count2 %>%
kbl() %>%
kable_paper("hover", full_width = F)| CTC1 | CTC10 | CTC11 | CTC12 | CTC13 | CTC14 | CTC15 | CTC2 | CTC3 | CTC4 | CTC5 | CTC6 | CTC7 | CTC9 | NP41 | S104 | S25 | S27 | S30 | S32 | S36 | S37 | S38 | S39 | S44 | S48 | S49 | S51 | S77 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| oligotype_A (27) | size:6013 / node_N1114 (27) | size:6013 | 10 | 24 | 10 | 7 | 442 | 3 | 10 | 3 | 4 | 8 | 0 | 26 | 2 | 2 | 0 | 4 | 3 | 2 | 1200 | 965 | 55 | 2132 | 46 | 666 | 17 | 2 | 43 | 13 | 314 |
| oligotype_G (23) | size:2498 / node_N1116 (23) | size:2498 | 4 | 6 | 1 | 0 | 207 | 0 | 7 | 2 | 2 | 5 | 1 | 5 | 2 | 1 | 40 | 0 | 5 | 0 | 655 | 298 | 36 | 874 | 7 | 306 | 6 | 0 | 23 | 5 | 0 |
## edit the fasta dataframe
fasta2 <- fasta2 %>% merge(ref_oligo_med2 %>% dplyr::select(-c(seq)), by="oligotype")
rownames(fasta2) <- fasta2$ref
fasta2 <- fasta2 %>% dplyr::select(-c(MED_node_frequency_size, OLIGO_oligotype_frequency_size, oligotype))
## print it
fasta2 %>%
kbl() %>%
kable_paper("hover", full_width = F)| ref | |
|---|---|
| oligotype_A (27) | size:6013 / node_N1114 (27) | size:6013 | oligotype_A (27) | size:6013 / node_N1114 (27) | size:6013 |
| oligotype_G (23) | size:2498 / node_N1116 (23) | size:2498 | oligotype_G (23) | size:2498 / node_N1116 (23) | size:2498 |
Metadata
metadata <- read.csv("../../../../metadata/metadata.csv", sep=";")
rownames(metadata) <- metadata$SamplePhyloseq object with oligotypes
# convert matrix_count into matrix and numeric
matrix_count <- matrix_count2 %>% as.matrix()
class(matrix_count) <- "numeric"
# phyloseq elements
OTU = otu_table(as.matrix(matrix_count), taxa_are_rows =TRUE)
TAX = tax_table(as.matrix(fasta2))
SAM = sample_data(metadata)
# phyloseq object
ps <- phyloseq(OTU, TAX, SAM)
ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2 taxa and 29 samples ]
## sample_data() Sample Data: [ 29 samples by 8 sample variables ]
## tax_table() Taxonomy Table: [ 2 taxa by 1 taxonomic ranks ]compute_read_counts(ps)## [1] 8511# remove blanks
ps <- subset_samples(ps, Strain!="Blank")
ps <- check_ps(ps)
ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2 taxa and 28 samples ]
## sample_data() Sample Data: [ 28 samples by 8 sample variables ]
## tax_table() Taxonomy Table: [ 2 taxa by 1 taxonomic ranks ]Create new metadata with Percent
Load ps with all samples (for final plot)
ps.filter <- readRDS("../../../../output/1_MED/1D/1D_MED_phyloseq_decontam.rds")
ps.filter <- check_ps(ps.filter)Edit new metadata with Percent_serratia
guide_italics <- guides(fill = guide_legend(label.theme = element_text(size = 16, face = "italic", colour = "Black", angle = 0)))
# add read depth in sample table of phyloseq object
sample_data(ps.filter)$Read_depth <- sample_sums(ps.filter)
# select Serratia
ps.serratia <- ps.filter %>% subset_taxa(Genus=="Serratia")
# add read depth of Serratia
sample_data(ps.filter)$Read_serratia <- sample_sums(ps.serratia)
sample_data(ps.filter) %>% colnames()## [1] "Sample" "Strain" "Field" "Country"
## [5] "Organ" "Species" "Run" "Control"
## [9] "Species_italic" "Strain_italic" "Read_depth" "is.neg"
## [13] "Read_serratia"sample_data(ps.serratia) %>% colnames()## [1] "Sample" "Strain" "Field" "Country"
## [5] "Organ" "Species" "Run" "Control"
## [9] "Species_italic" "Strain_italic" "Read_depth" "is.neg"# add percent of Serratia
sample_data(ps.filter)$Percent_serratia <- sample_data(ps.filter)$Read_serratia / sample_data(ps.filter)$Read_depth
# round the percent of Serratia at 2 decimals
sample_data(ps.filter)$Percent_serratia <- sample_data(ps.filter)$Percent_serratia %>% round(2)
# extract metadata table
test <- data.frame(sample_data(ps.filter))
# merge this metadata table with the other
new.metadata <- data.frame(sample_data(ps)) %>% merge(test %>% dplyr::select(c(Sample, Read_depth, Read_serratia, Percent_serratia)), by="Sample")
new.metadata <- test[new.metadata$Sample %in% sample_names(ps),]
rownames(new.metadata) <- new.metadata$Sample
# print it
new.metadata %>%
kbl() %>%
kable_paper("hover", full_width = F)| Sample | Strain | Field | Country | Organ | Species | Run | Control | Species_italic | Strain_italic | Read_depth | is.neg | Read_serratia | Percent_serratia | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CTC1 | CTC1 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 29779 | FALSE | 14 | 0.00 |
| CTC10 | CTC10 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 2609 | FALSE | 30 | 0.01 |
| CTC11 | CTC11 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 13874 | FALSE | 11 | 0.00 |
| CTC12 | CTC12 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 1146 | FALSE | 7 | 0.01 |
| CTC13 | CTC13 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 18035 | FALSE | 649 | 0.04 |
| CTC14 | CTC14 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 1708 | FALSE | 3 | 0.00 |
| CTC15 | CTC15 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 23180 | FALSE | 17 | 0.00 |
| CTC2 | CTC2 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 30692 | FALSE | 5 | 0.00 |
| CTC3 | CTC3 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 39920 | FALSE | 6 | 0.00 |
| CTC4 | CTC4 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 2139 | FALSE | 13 | 0.01 |
| CTC5 | CTC5 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 15789 | FALSE | 1 | 0.00 |
| CTC6 | CTC6 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 19753 | FALSE | 31 | 0.00 |
| CTC9 | CTC9 | Laboratory - Slab TC (Wolbachia -) | Lab | France | Whole | Culex quinquefasciatus | run2 | True sample | italic(“Culex quinquefasciatus”) | paste(“Laboratory - Slab TC (”, italic(“Wolbachia”), “-)”) | 4980 | FALSE | 3 | 0.00 |
| NP14 | NP14 | Field - Guadeloupe | Field | Guadeloupe | Ovary | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 7973 | FALSE | 0 | 0.00 |
| NP2 | NP2 | Field - Guadeloupe | Field | Guadeloupe | Ovary | Culex quinquefasciatus | run3 | True sample | italic(“Culex quinquefasciatus”) | Field-Guadeloupe | 648335 | FALSE | 0 | 0.00 |
| NP20 | NP20 | Field - Guadeloupe | Field | Guadeloupe | Ovary | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 136 | FALSE | 0 | 0.00 |
| NP27 | NP27 | Field - Guadeloupe | Field | Guadeloupe | Whole | Culex quinquefasciatus | run3 | True sample | italic(“Culex quinquefasciatus”) | Field-Guadeloupe | 1234 | FALSE | 0 | 0.00 |
| NP29 | NP29 | Field - Guadeloupe | Field | Guadeloupe | Whole | Culex quinquefasciatus | run3 | True sample | italic(“Culex quinquefasciatus”) | Field-Guadeloupe | 203 | FALSE | 0 | 0.00 |
| NP30 | NP30 | Field - Guadeloupe | Field | Guadeloupe | Whole | Culex quinquefasciatus | run3 | True sample | italic(“Culex quinquefasciatus”) | Field-Guadeloupe | 228 | FALSE | 0 | 0.00 |
| NP34 | NP34 | Field - Guadeloupe | Field | Guadeloupe | Whole | Culex quinquefasciatus | run3 | True sample | italic(“Culex quinquefasciatus”) | Field-Guadeloupe | 95 | FALSE | 0 | 0.00 |
| NP35 | NP35 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 196532 | FALSE | 0 | 0.00 |
| NP36 | NP36 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 249 | FALSE | 0 | 0.00 |
| NP37 | NP37 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 419340 | FALSE | 0 | 0.00 |
| NP38 | NP38 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 282479 | FALSE | 0 | 0.00 |
| NP39 | NP39 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 218684 | FALSE | 0 | 0.00 |
| NP41 | NP41 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 247152 | FALSE | 40 | 0.00 |
| NP42 | NP42 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 185157 | FALSE | 0 | 0.00 |
| NP43 | NP43 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 239335 | FALSE | 0 | 0.00 |
| NP44 | NP44 | Field - Guadeloupe | Field | Guadeloupe | Whole | Aedes aegypti | run3 | True sample | italic(“Aedes aegypti”) | Field-Guadeloupe | 156879 | FALSE | 0 | 0.00 |
| NP5 | NP5 | Field - Guadeloupe | Field | Guadeloupe | Ovary | Culex quinquefasciatus | run3 | True sample | italic(“Culex quinquefasciatus”) | Field-Guadeloupe | 736159 | FALSE | 0 | 0.00 |
| NP8 | NP8 | Field - Guadeloupe | Field | Guadeloupe | Ovary | Culex quinquefasciatus | run3 | True sample | italic(“Culex quinquefasciatus”) | Field-Guadeloupe | 334799 | FALSE | 0 | 0.00 |
| S100 | S100 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 52486 | FALSE | 0 | 0.00 |
| S102 | S102 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 3456 | FALSE | 0 | 0.00 |
| S104 | S104 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 52403 | FALSE | 4 | 0.00 |
| S105 | S105 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 55577 | FALSE | 0 | 0.00 |
| S106 | S106 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 33053 | FALSE | 0 | 0.00 |
| S107 | S107 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 52154 | FALSE | 0 | 0.00 |
| S108 | S108 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 55735 | FALSE | 0 | 0.00 |
| S109 | S109 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 59023 | FALSE | 0 | 0.00 |
| S110 | S110 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 57377 | FALSE | 0 | 0.00 |
| S121 | S121 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 20361 | FALSE | 0 | 0.00 |
| S122 | S122 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 9803 | FALSE | 0 | 0.00 |
| S123 | S123 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 20130 | FALSE | 0 | 0.00 |
| S124 | S124 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 18146 | FALSE | 0 | 0.00 |
| S126 | S126 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 15235 | FALSE | 0 | 0.00 |
| S127 | S127 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 24696 | FALSE | 0 | 0.00 |
| S128 | S128 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 16305 | FALSE | 0 | 0.00 |
| S146 | S146 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 25012 | FALSE | 0 | 0.00 |
| S147 | S147 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 25171 | FALSE | 0 | 0.00 |
| S148 | S148 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 14164 | FALSE | 0 | 0.00 |
| S150 | S150 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 15081 | FALSE | 0 | 0.00 |
| S151 | S151 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 22944 | FALSE | 0 | 0.00 |
| S152 | S152 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 15082 | FALSE | 0 | 0.00 |
| S153 | S153 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 17040 | FALSE | 0 | 0.00 |
| S154 | S154 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 9626 | FALSE | 0 | 0.00 |
| S160 | S160 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 72508 | FALSE | 0 | 0.00 |
| S162 | S162 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 25180 | FALSE | 0 | 0.00 |
| S163 | S163 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 12333 | FALSE | 0 | 0.00 |
| S164 | S164 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 22368 | FALSE | 0 | 0.00 |
| S165 | S165 | Laboratory - Lavar | Lab | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 17731 | FALSE | 0 | 0.00 |
| S166 | S166 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 13979 | FALSE | 0 | 0.00 |
| S167 | S167 | Field - Bosc | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Bosc | 14048 | FALSE | 0 | 0.00 |
| S169 | S169 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 11553 | FALSE | 0 | 0.00 |
| S170 | S170 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run2 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 8852 | FALSE | 0 | 0.00 |
| S18 | S18 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 4290 | FALSE | 0 | 0.00 |
| S19 | S19 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 44527 | FALSE | 0 | 0.00 |
| S20 | S20 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 42864 | FALSE | 0 | 0.00 |
| S21 | S21 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 33798 | FALSE | 0 | 0.00 |
| S22 | S22 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 19044 | FALSE | 0 | 0.00 |
| S23 | S23 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 38172 | FALSE | 0 | 0.00 |
| S24 | S24 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 42355 | FALSE | 0 | 0.00 |
| S25 | S25 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 47688 | FALSE | 8 | 0.00 |
| S26 | S26 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 5394 | FALSE | 0 | 0.00 |
| S27 | S27 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 24558 | FALSE | 2 | 0.00 |
| S28 | S28 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 4503 | FALSE | 0 | 0.00 |
| S30 | S30 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 25353 | FALSE | 1855 | 0.07 |
| S31 | S31 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 20417 | FALSE | 0 | 0.00 |
| S32 | S32 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 12441 | FALSE | 1263 | 0.10 |
| S33 | S33 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 33867 | FALSE | 0 | 0.00 |
| S34 | S34 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 9367 | FALSE | 0 | 0.00 |
| S35 | S35 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 11663 | FALSE | 0 | 0.00 |
| S36 | S36 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 33020 | FALSE | 91 | 0.00 |
| S37 | S37 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 18340 | FALSE | 3006 | 0.16 |
| S38 | S38 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 54790 | FALSE | 53 | 0.00 |
| S39 | S39 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 36273 | FALSE | 972 | 0.03 |
| S40 | S40 | Laboratory - Lavar | Lab | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Laboratory-Lavar | 44448 | FALSE | 0 | 0.00 |
| S42 | S42 | Field - Camping Europe | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 4107 | FALSE | 0 | 0.00 |
| S43 | S43 | Field - Camping Europe | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 9279 | FALSE | 0 | 0.00 |
| S44 | S44 | Field - Camping Europe | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 8026 | FALSE | 23 | 0.00 |
| S45 | S45 | Field - Camping Europe | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 18150 | FALSE | 0 | 0.00 |
| S47 | S47 | Field - Camping Europe | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 1951 | FALSE | 0 | 0.00 |
| S48 | S48 | Field - Camping Europe | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 56738 | FALSE | 2 | 0.00 |
| S49 | S49 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 33498 | FALSE | 66 | 0.00 |
| S50 | S50 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 28481 | FALSE | 0 | 0.00 |
| S51 | S51 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 61788 | FALSE | 18 | 0.00 |
| S52 | S52 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 21553 | FALSE | 0 | 0.00 |
| S55 | S55 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 50447 | FALSE | 0 | 0.00 |
| S56 | S56 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 42609 | FALSE | 0 | 0.00 |
| S57 | S57 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 49157 | FALSE | 0 | 0.00 |
| S58 | S58 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 30357 | FALSE | 0 | 0.00 |
| S59 | S59 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 32798 | FALSE | 0 | 0.00 |
| S60 | S60 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 44485 | FALSE | 0 | 0.00 |
| S61 | S61 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 49545 | FALSE | 0 | 0.00 |
| S63 | S63 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 53444 | FALSE | 0 | 0.00 |
| S64 | S64 | Field - Bosc | Field | France | Whole | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 47628 | FALSE | 0 | 0.00 |
| S79 | S79 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 59755 | FALSE | 0 | 0.00 |
| S80 | S80 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 52788 | FALSE | 0 | 0.00 |
| S83 | S83 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 42272 | FALSE | 0 | 0.00 |
| S84 | S84 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 56676 | FALSE | 0 | 0.00 |
| S85 | S85 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 41690 | FALSE | 0 | 0.00 |
| S86 | S86 | Field - Camping Europe | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Camping~Europe | 61984 | FALSE | 0 | 0.00 |
| S87 | S87 | Field - Bosc | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 65958 | FALSE | 0 | 0.00 |
| S88 | S88 | Field - Bosc | Field | France | Ovary | Culex pipiens | run1 | True sample | italic(“Culex pipiens”) | Field-Bosc | 53102 | FALSE | 0 | 0.00 |
# replace metadata in the created phyloseq object
sample_data(ps) <- sample_data(new.metadata)Taxonomic structure
Count
col <- brewer.pal(7, "Pastel2")
# reshape data for plot
test3 <- test %>% select(c(Sample, Species, Strain, Organ, Read_depth, Read_serratia)) %>% reshape2::melt(id.vars=c("Sample", "Species", "Strain", "Organ"), vars=c("Read_depth", "Read_serratia"))
count_whole <- test3[test3$Organ=="Whole",]
count_ovary <- test3[test3$Organ=="Ovary",]
make.italic <- function(x) as.expression(lapply(x, function(y) bquote(italic(.(y)))))
levels(count_whole$Species)= c("Aedes aegypti"=make.italic("Aedes aegypti"),
"Culex pipiens"=make.italic("Culex pipiens"),
"Culex quinquefasciatus"=make.italic("Culex quinquefasciatus"))
levels(count_ovary$Species)= c("Aedes aegypti"=make.italic("Aedes aegypti"),
"Culex pipiens"=make.italic("Culex pipiens"),
"Culex quinquefasciatus"=make.italic("Culex quinquefasciatus"))
levels(count_whole$Strain) <- c("Bosc", "Camping~Europe", "Guadeloupe", "Lavar~(lab)", expression(paste(italic("Wolbachia"), "- (Slab TC)")))
levels(count_ovary$Strain) <- c("Bosc", "Camping~Europe", "Guadeloupe", "Lavar~(lab)", expression(paste(italic("Wolbachia"), "- (Slab TC)")))
# plot
p_count1 <- ggplot(count_whole, aes(x = Sample, y = value, fill=variable))+
geom_bar(position = "dodge", stat = "identity")+
scale_fill_manual(values = col)+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, size=12, hjust=1, vjust=0.5)) +
ggtitle("") +
guide_italics+
theme(legend.title = element_text(size = 20),
legend.position="bottom",
legend.text=element_text(size=14),
panel.spacing.y=unit(1, "lines"),
panel.spacing.x=unit(0.8, "lines"),
panel.spacing=unit(0,"lines"),
strip.background=element_rect(color="grey30", fill="grey90"),
strip.text.x = element_text(size = 16),
panel.border=element_rect(color="grey90"),
axis.ticks.x=element_blank(),
axis.text.y = element_text(size=18)) +
facet_wrap(~Species+Strain+Organ, scales = "free_x", ncol=3, labeller=label_parsed)+
labs(y="Sequence counts")+
ylim(0, 900000)+
geom_text(aes(label=value), position=position_dodge(width=1.1), width=0.25, size=4, hjust=-0.25, vjust=0.5, angle=90)+
guides(fill=guide_legend(title="Read"))## Warning: Ignoring unknown parameters: widthp_count2 <- ggplot(count_ovary, aes(x = Sample, y = value, fill=variable))+
geom_bar(position = "dodge", stat = "identity")+
scale_fill_manual(values = col)+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, size=18, hjust=1, vjust=0.5)) +
ggtitle("") +
guide_italics+
theme(legend.title = element_text(size = 20),
legend.position="bottom",
legend.text=element_text(size=14),
panel.spacing.y=unit(1, "lines"),
panel.spacing.x=unit(0.8, "lines"),
panel.spacing=unit(0,"lines"),
strip.background=element_rect(color="grey30", fill="grey90"),
strip.text.x = element_text(size = 16),
panel.border=element_rect(color="grey90"),
axis.ticks.x=element_blank(),
axis.text.y = element_text(size=18)) +
facet_wrap(~Species+Strain+Organ, scales = "free_x", ncol=3, labeller=label_parsed)+
labs(y="Sequence counts")+
ylim(0, 900000)+
geom_text(aes(label=value), position=position_dodge(width=0.8), width=0.25, size=4, hjust=-0.25, vjust=0.5, angle=90)+
guides(fill=guide_legend(title="Read"))## Warning: Ignoring unknown parameters: width# afficher plot
p_count1## Warning: position_dodge requires non-overlapping x intervals## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
p_count2
# panels
p_group <- plot_grid(p_count1+theme(legend.position="none"),
p_count2+theme(legend.position="none"),
nrow=2,
ncol=1)+
draw_plot_label(c("B1", "B2"), c(0, 0), c(1, 0.5), size = 20)## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervalslegend_plot <- get_legend(p_count1 + theme(legend.position="bottom"))## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervalsp_counts <- plot_grid(p_group, legend_plot, nrow=2, ncol=1, rel_heights = c(1, .1))
p_counts
Whole (the most abundant nodes)
guide_italics <- guides(fill = guide_legend(label.theme = element_text(size = 16, face = "italic", colour = "Black", angle = 0),
nrow=2, byrow=TRUE))
# select whole
ps.filter.whole <- subset_samples(ps, Organ=="Whole")
ps.filter.whole <- prune_taxa(taxa_sums(ps.filter.whole) >= 1, ps.filter.whole)
ps.filter.whole <- prune_samples(sample_sums(ps.filter.whole) >= 1, ps.filter.whole)
ps.filter.whole## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2 taxa and 26 samples ]
## sample_data() Sample Data: [ 26 samples by 14 sample variables ]
## tax_table() Taxonomy Table: [ 2 taxa by 1 taxonomic ranks ]# data pour plot
#data_for_plot2 <- taxo_data_fast(ps.filter.whole, method = "abundance")
data_for_plot2 <- taxo_data(ps.filter.whole, top=15)## Warning in psmelt(ps_global): The sample variables:
## Sample
## have been renamed to:
## sample_Sample
## to avoid conflicts with special phyloseq plot attribute names.paste0("\n15 MOST ABUNDANT GENUS: \n") %>% cat()##
## 15 MOST ABUNDANT GENUS:paste0("\"", levels(data_for_plot2$Name), "\",\n") %>% cat()## "oligotype_A (27) | size:6013 / node_N1114 (27) | size:6013.",
## "oligotype_G (23) | size:2498 / node_N1116 (23) | size:2498.",data_for_plot2$Name <- data_for_plot2$Name %>% gsub(pattern = "node_", replacement ="" ) %>% as.factor()
data_for_plot2$Name <- as.factor(data_for_plot2$Name)
new_names <- c("oligotype_A (27) | size:6013 / N1114 (27) | size:6013.",
"oligotype_G (23) | size:2498 / N1116 (23) | size:2498."
)
data_for_plot2$Name <- factor(data_for_plot2$Name, levels = new_names)
col_add <- brewer.pal(8, "Accent")
col <- c("oligotype_A (27) | size:6013 / N1114 (27) | size:6013."="#B136F5",
"oligotype_G (23) | size:2498 / N1116 (23) | size:2498."="#B863FF")
levels(data_for_plot2$Species)= c("Aedes aegypti"=make.italic("Aedes aegypti"),
"Culex pipiens"=make.italic("Culex pipiens"),
"Culex quinquefasciatus"=make.italic("Culex quinquefasciatus"))
levels(data_for_plot2$Strain) <- c("Bosc", "Camping~Europe", "Guadeloupe", "Lavar~(lab)", expression(paste(italic("Wolbachia"), "- (Slab TC)")))
#data_for_plot2 <- data_for_plot2 %>% na.omit()
p2 <- ggplot(data_for_plot2, aes(x = Sample, y = Relative_Abundance, fill = Name, species=Species, organ=Organ, Strain=Strain))+
geom_bar(position = "stack", stat = "identity")+
scale_fill_manual(values = col)+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, size=18, hjust=1, vjust=0.5)) +
ggtitle("") +
guide_italics+
theme(legend.title = element_text(size = 20),
legend.position="bottom",
legend.text = element_text(size=14),
#legend.key.height = unit(1, 'cm'),
panel.spacing.y=unit(1, "lines"),
panel.spacing.x=unit(0.8, "lines"),
panel.spacing=unit(0,"lines"),
strip.background=element_rect(color="grey30", fill="grey90"),
strip.text.x = element_text(size = 16),
panel.border=element_rect(color="grey90"),
axis.ticks.x=element_blank(),
axis.text.y = element_text(size=18)) +
facet_wrap(~Species+Strain+Organ, scales = "free", ncol=3, labeller=label_parsed)+
labs(x="Sample", y="Relative abundance", fill="Oligotype / MED node")
p2
Ovary (the most abundant nodes)
# select ovary
ps.filter.ovary <- subset_samples(ps, Organ=="Ovary")
ps.filter.ovary <- prune_taxa(taxa_sums(ps.filter.ovary) >= 1, ps.filter.ovary)
ps.filter.ovary <- prune_samples(sample_sums(ps.filter.ovary) >= 1, ps.filter.ovary)
ps.filter.ovary## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1 taxa and 1 samples ]
## sample_data() Sample Data: [ 1 samples by 14 sample variables ]
## tax_table() Taxonomy Table: [ 1 taxa by 1 taxonomic ranks ]# data pour plot
data_for_plot3 <- taxo_data(ps.filter.ovary, top=15)## Warning in psmelt(ps_global): The sample variables:
## Sample
## have been renamed to:
## sample_Sample
## to avoid conflicts with special phyloseq plot attribute names.paste0("\n15 MOST ABUNDANT GENUS: \n") %>% cat()##
## 15 MOST ABUNDANT GENUS:paste0("\"", levels(data_for_plot3$Name), "\",\n") %>% cat()## "oligotype_A (27) | size:6013 / node_N1114 (27) | size:6013.",data_for_plot3$Name <- data_for_plot3$Name %>% gsub(pattern = "node_", replacement ="" ) %>% as.factor()
data_for_plot3$Name <- as.factor(data_for_plot3$Name)
levels(data_for_plot3$Species)= c("Culex pipiens"=make.italic("Culex pipiens"))
levels(data_for_plot3$Strain) <- c("Camping~Europe")
p3 <- ggplot(data_for_plot3, aes(x = Sample, y = Relative_Abundance, fill = Name, species=Species, organ=Organ, Strain=Strain))+
geom_bar(position = "stack", stat = "identity")+
scale_fill_manual(values = col)+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, size=18, hjust=1, vjust=0.5)) +
ggtitle("") +
guide_italics+
theme(legend.title = element_text(size = 20),
legend.position="bottom",
legend.text = element_text(size=14),
#legend.key.height = unit(1, 'cm'),
panel.spacing.y=unit(1, "lines"),
panel.spacing.x=unit(0.8, "lines"),
panel.spacing=unit(0,"lines"),
strip.background=element_rect(color="grey30", fill="grey90"),
strip.text.x = element_text(size = 16),
panel.border=element_rect(color="grey90"),
axis.ticks.x=element_blank(),
axis.text.y = element_text(size=18)) +
facet_wrap(~Species+Strain+Organ, scales = "free", ncol=3, labeller=label_parsed)+
labs(x="Sample", y="Relative abundance", fill="Oligotype / MED node")
p3
Panels taxonomy of whole / ovary
legend_plot <- get_legend(p2 + theme(legend.position="bottom"))
# panels
p_group <- plot_grid(p2+theme(legend.position="none"),
p3+theme(legend.position="none"),
nrow=2,
ncol=1)+
draw_plot_label(c("A1", "A2"), c(0, 0), c(1, 0.5), size = 20)
p_taxo <- plot_grid(p_group, legend_plot, nrow=2, rel_heights = c(1, .1))
p_taxo
Save taxonomic plot
tiff("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_counts_serratia.tiff", units="in", width=20, height=18, res=300)
p_counts
dev.off()## quartz_off_screen
## 2tiff("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_taxonomic_serratia_whole.tiff", units="in", width=16, height=12, res=300)
p2
dev.off()## quartz_off_screen
## 2tiff("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_taxonomic_serratia_ovary.tiff", units="in", width=18, height=14, res=300)
p3
dev.off()## quartz_off_screen
## 2tiff("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_taxonomic_serratia.tiff", units="in", width=18, height=16, res=300)
p_taxo
dev.off()## quartz_off_screen
## 2png("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_counts_serratia_big.png", units="in", width=20, height=18, res=300)
p_counts
dev.off()## quartz_off_screen
## 2png("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_counts_serratia_small.png", units="in", width=18, height=14, res=300)
p_counts
dev.off()## quartz_off_screen
## 2png("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_taxonomic_serratia_whole.png", units="in", width=16, height=12, res=300)
p2
dev.off()## quartz_off_screen
## 2png("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_taxonomic_serratia_ovary.png", units="in", width=18, height=14, res=300)
p3
dev.off()## quartz_off_screen
## 2png("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_taxonomic_serratia_big.png", units="in", width=18, height=18, res=300)
p_taxo
dev.off()## quartz_off_screen
## 2png("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_taxonomic_serratia_small.png", units="in", width=18, height=14, res=300)
p_taxo
dev.off()## quartz_off_screen
## 2Make main plot
img <- magick::image_read(paste0(path_serratia, "/HTML-OUTPUT/entropy.png"))
p_entropy <- magick::image_ggplot(img, interpolate = TRUE)
p_entropy+ theme(plot.margin = unit(c(-7,-2.5,-7,-0.5), "cm"))
p_entropy+ theme(plot.margin=unit(c(-7,-2,-12,-5), "mm"))
aligned <- plot_grid(p_taxo,
p_counts,
align="hv")
aligned
p_entropy2 <- plot_grid(p_entropy, nrow=1)+
draw_plot_label(c("C"), c(0), c(1), size=20, hjust=-0.5)
p_entropy2
t_plot <- plot_grid(aligned,
p_entropy2,
nrow=2,
ncol=1,
scale=1,
rel_heights=c(2,1))
t_plot
tiff("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_main_serratia.tiff", width=36, height=36, res=300, units="in")
t_plot
dev.off()## quartz_off_screen
## 2png("../../../../output/2_Oligotyping/2D/2Dg_OLIGO_main_serratia.png", width=36, height=36, res=300, units="in")
t_plot
dev.off()## quartz_off_screen
## 2Session info
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] kableExtra_1.3.1 openxlsx_4.2.3 Biostrings_2.54.0
## [4] XVector_0.26.0 IRanges_2.20.2 S4Vectors_0.24.4
## [7] BiocGenerics_0.32.0 cowplot_1.1.0 ggpubr_0.4.0
## [10] RColorBrewer_1.1-2 forcats_0.5.0 stringr_1.4.0
## [13] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
## [16] tidyr_1.1.2 tibble_3.0.4 tidyverse_1.3.0
## [19] ggplot2_3.3.2 phyloseq_1.30.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-0 ggsignif_0.6.0 ellipsis_0.3.1 rio_0.5.16
## [5] fs_1.5.0 rstudioapi_0.11 farver_2.0.3 fansi_0.4.1
## [9] lubridate_1.7.9 xml2_1.3.2 codetools_0.2-16 splines_3.6.3
## [13] knitr_1.30 ade4_1.7-15 jsonlite_1.7.1 broom_0.7.2
## [17] cluster_2.1.0 dbplyr_1.4.4 compiler_3.6.3 httr_1.4.2
## [21] backports_1.1.10 assertthat_0.2.1 Matrix_1.2-18 cli_2.1.0
## [25] htmltools_0.5.1.1 tools_3.6.3 igraph_1.2.6 gtable_0.3.0
## [29] glue_1.4.2 reshape2_1.4.4 Rcpp_1.0.5 carData_3.0-4
## [33] Biobase_2.46.0 cellranger_1.1.0 jquerylib_0.1.3 vctrs_0.3.4
## [37] multtest_2.42.0 ape_5.4-1 nlme_3.1-149 iterators_1.0.13
## [41] xfun_0.22 ps_1.4.0 rvest_0.3.6 lifecycle_0.2.0
## [45] rstatix_0.6.0 zlibbioc_1.32.0 MASS_7.3-53 scales_1.1.1
## [49] hms_0.5.3 biomformat_1.14.0 rhdf5_2.30.1 yaml_2.2.1
## [53] curl_4.3 sass_0.3.1 stringi_1.5.3 highr_0.8
## [57] foreach_1.5.1 permute_0.9-5 zip_2.1.1 rlang_0.4.10
## [61] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41 Rhdf5lib_1.8.0
## [65] labeling_0.4.2 tidyselect_1.1.0 plyr_1.8.6 magrittr_1.5
## [69] bookdown_0.22 R6_2.4.1 magick_2.5.2 generics_0.0.2
## [73] DBI_1.1.0 pillar_1.4.6 haven_2.3.1 foreign_0.8-75
## [77] withr_2.3.0 mgcv_1.8-33 survival_3.2-7 abind_1.4-5
## [81] modelr_0.1.8 crayon_1.3.4 car_3.0-10 rmarkdown_2.7
## [85] grid_3.6.3 readxl_1.3.1 data.table_1.13.2 blob_1.2.1
## [89] vegan_2.5-6 rmdformats_1.0.2 reprex_0.3.0 digest_0.6.26
## [93] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0 bslib_0.2.4